IDS_CDS_ADS
Elin Larsen
10/05/2018
Paths to modify
path_to_CDI="/Users/elinlarsen/GoogleDrive/PhD_elin/Projets/CDSwordseg_Pipeline/CDI/english/"
res='/Users/elinlarsen/GoogleDrive/PhD_elin/Projets/CDSwordSeg_Pipeline/results/'
path_to_figures="/Users/elinlarsen/GoogleDrive/PhD_elin/our_papers/CogSci2018/figures/mai2018/"
#depends on the data architecture
res_brent=paste(res, 'Brent/full_corpus', sep='/')
res_providence=paste(res, 'Providence/full_corpus', sep='/')
res_buckeye=paste(res, 'buckeye', sep='/')For quick reproduction : skip this and go to the chunk load cdi-algo-data
else clean environment, modify paths and clean each script ### ALGOS parameters
algos=c('tp/relativeforward', 'tp/absoluteforward', 'tp/relativebackward', 'tp/absolutebackward','dibs','puddle','ag')
unit=c("phoneme", "syllable")
Asyll=paste(algos,"syllable", sep="/")
Aph=paste(algos,"phoneme", sep="/")
AU=c(Asyll, Aph)
AU_g=list.append(AU,"gold")Data processing
brent=read_gold(res_brent, "brent")## # A tibble: 6,366 x 2
## count Type
## <int> <chr>
## 1 24727 you
## 2 11233 the
## 3 7721 a
## 4 7518 it
## 5 6323 that
## 6 6048 your
## 7 5552 are
## 8 5519 no
## 9 5439 i
## 10 5154 and
## # ... with 6,356 more rowsprovidence=read_gold(res_providence, "providence")## # A tibble: 6,589 x 2
## count Type
## <int> <chr>
## 1 11003 the
## 2 10552 you
## 3 7233 a
## 4 5038 and
## 5 4456 it
## 6 4217 i
## 7 4179 to
## 8 3969 that
## 9 3663 are
## 10 3527 m@l
## # ... with 6,579 more rowsbuckeye=read_gold(res_buckeye, "buckeye")## # A tibble: 9,189 x 2
## count Type
## <int> <chr>
## 1 12391 i
## 2 10903 and
## 3 8314 the
## 4 8023 you
## 5 6393 to
## 6 6170 know
## 7 6088 a
## 8 5342 that
## 9 4595 it
## 10 4059 like
## # ... with 9,179 more rowsfreq_algos_brent=read_algorithms_results(res_brent, algos, res_brent, 'brent')## [1] 6366freq_algos_providence=read_algorithms_results(res_providence , algos, res_providence, 'providence')## [1] 6589freq_algos_buckeye=read_algorithms_results(res_buckeye, algos, res_buckeye, 'buckeye')## [1] 9189freq_all=dplyr::bind_rows(brent, freq_algos_brent, providence, freq_algos_providence , buckeye, freq_algos_buckeye)%>%
mutate(uni_lemma=Type)%>%
select(-Type)
#knitr::kable(head(freq_all), format = "html")%>%
#kable_styling(bootstrap_options = c("striped", "hover"))Check number of words per algos
for (a in AU_g)
{
X<-freq_all%>%
filter(au==a, corpus=="brent")%>%
group_by(uni_lemma, algos)%>%
n_groups()
print(paste(a, X, sep= ": "))
}## [1] "tp/relativeforward/syllable: 6366"
## [1] "tp/absoluteforward/syllable: 6366"
## [1] "tp/relativebackward/syllable: 6366"
## [1] "tp/absolutebackward/syllable: 6366"
## [1] "dibs/syllable: 6366"
## [1] "puddle/syllable: 6366"
## [1] "ag/syllable: 6371"
## [1] "tp/relativeforward/phoneme: 6366"
## [1] "tp/absoluteforward/phoneme: 6366"
## [1] "tp/relativebackward/phoneme: 6366"
## [1] "tp/absolutebackward/phoneme: 6366"
## [1] "dibs/phoneme: 6366"
## [1] "puddle/phoneme: 6366"
## [1] "ag/phoneme: 6374"
## [1] "gold: 6366"Stemming : -> FOR LATER ANALYSIS
Not working for now
#freq_all$lemma=stem(freq_all$Type, "english") last version of CDI from Braginsky 2018
load(paste('/Users/elinlarsen/GoogleDrive/PhD_elin/Projets/CDSwordseg_Pipeline/CDI/', 'uni_prop_data.RData', sep="/"), .GlobalEnv)
new_prop<-uni_prop_data%>%
filter(language=="English (American)")
#linguistics<-d_prop%>%
#mutate(uni_lemma=Type, measure=form)%>%
#select(-prop)
#linguistics$measure[linguistics$measure=="WG_production"]="produces"
#linguistics$measure[linguistics$measure=="WS_production"]="produces"
#linguistics$measure[linguistics$measure=="WG_comprehension"]="understands"
#new_prop=merge(new_prop_eng, linguistics, by=c("uni_lemma", "age", "measure"))
head(new_prop)## # A tibble: 6 x 8
## # Groups: language, measure, uni_lemma [1]
## language measure uni_lemma age num_true num_false prop items
## <chr> <chr> <chr> <int> <int> <int> <dbl> <list>
## 1 English (Am… produc… airplane 8 0 35 0 <tibble…
## 2 English (Am… produc… airplane 9 0 94 0 <tibble…
## 3 English (Am… produc… airplane 10 1 154 0.00645 <tibble…
## 4 English (Am… produc… airplane 11 3 91 0.0319 <tibble…
## 5 English (Am… produc… airplane 12 3 140 0.0210 <tibble…
## 6 English (Am… produc… airplane 13 31 739 0.0403 <tibble…Number of uni_lemma types
new_prop%>%
group_by(uni_lemma)%>%
n_groups()## [1] 395Merge cdi data and algo data by TYPE
new=merge(new_prop,freq_all)%>%
select(-items, -num_true, -num_false)
new$log_freq=log(new$freq_smoothed)
new$log_count=log(new$count+1)
knitr::kable(head(new), format = "html")%>%
kable_styling(bootstrap_options = c("striped", "hover"))| uni_lemma | language | measure | age | prop | count | count_gold | unit | algos | freq_smoothed | corpus | au | log_freq | log_count |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| airplane | English (American) | understands | 12 | 0.2097902 | 0 | 8 | phoneme | puddle | 3.70e-06 | buckeye | puddle/phoneme | -12.504324 | 0.000000 |
| airplane | English (American) | understands | 12 | 0.2097902 | 2 | 8 | syllable | tp/absolutebackward | 2.00e-05 | providence | tp/absolutebackward/syllable | -10.820771 | 1.098612 |
| airplane | English (American) | understands | 12 | 0.2097902 | 0 | 8 | syllable | dibs | 3.70e-06 | providence | dibs/syllable | -12.513851 | 0.000000 |
| airplane | English (American) | understands | 12 | 0.2097902 | 0 | 47 | phoneme | tp/relativeforward | 3.50e-06 | brent | tp/relativeforward/phoneme | -12.565166 | 0.000000 |
| airplane | English (American) | understands | 12 | 0.2097902 | 8 | 8 | gold | 2.88e-05 | providence | gold | -10.454704 | 2.197225 | |
| airplane | English (American) | understands | 12 | 0.2097902 | 5 | 8 | syllable | tp/relativeforward | 8.57e-05 | providence | tp/relativeforward/syllable | -9.364334 | 1.791759 |
Save this temporary data :
#save(new, file = paste(res, "/braginsky_cdi_algo_combined.RData",sep=""))Load data frame containing word segmentation algorithm results and CDI data CLEAN
#load(new, file = paste(res, "/braginsky_cdi_algo_combined.RData",sep=""))Check number of CDI words per algos
for (a in AU_g)
{
X<-new%>%
filter(au==a)%>%
group_by(uni_lemma, algos)%>%
n_groups()
print(paste(a, X, sep= ": "))
}## [1] "tp/relativeforward/syllable: 336"
## [1] "tp/absoluteforward/syllable: 336"
## [1] "tp/relativebackward/syllable: 336"
## [1] "tp/absolutebackward/syllable: 336"
## [1] "dibs/syllable: 336"
## [1] "puddle/syllable: 336"
## [1] "ag/syllable: 336"
## [1] "tp/relativeforward/phoneme: 336"
## [1] "tp/absoluteforward/phoneme: 336"
## [1] "tp/relativebackward/phoneme: 336"
## [1] "tp/absolutebackward/phoneme: 336"
## [1] "dibs/phoneme: 336"
## [1] "puddle/phoneme: 336"
## [1] "ag/phoneme: 336"
## [1] "gold: 336"Model parameters
AGES_comp=seq(8,18)
AGES_prod=seq(8,30)
CORPUS=c("brent", "providence", "buckeye")
MEASURE=c( 'understands')
if(MEASURE=="produces"){AGES=AGES_prod} else(AGES=AGES_comp)## [1] 8 9 10 11 12 13 14 15 16 17 18AU_g=list.append(AU,"gold")
G=c("gold")
DATA=newLinear models
by corpus-measure-algo-unit-age
R2_by_parameters=function(DATA, AGES, AG_g, CORPUS, MEASURE)
{
df=setNames(data.frame(matrix(ncol = 8, nrow = length(AGES)*length(AU_g)*length(CORPUS)*length(MEASURE))), c("age", "corpus", "measure", "au", "algo", "unit", "R2", "nbtokens"))
index=0
for (c in CORPUS)
{
for (f in MEASURE)
{
for (a in AGES)
{
for (A in AU_g)
{
index=index+1
#print(paste(a, A, f, c, sep=" "))
d<-DATA%>%
filter(au %in% A, age %in% a, corpus %in% c, measure%in% f)
nb_tokens=d%>%
group_by(uni_lemma)%>%
n_groups()
model=lm(prop ~ log(freq_smoothed), data=d)
df$corpus[index]=c
df$measure[index]=f
df$age[index]=a
df$au[index]=A
if(A!="gold"){
aa=head(strsplit(A[1], "/")[[1]], -1)
if (length(aa)==2){df$algo[index]=paste(aa[1], aa[2], sep="/")}
else(df$algo[index]=aa)
df$unit[index]=last(strsplit(A[1], "/")[[1]])}
if(A=="gold"){
df$algo[index]=A
df$unit[index]=" "
}
df$R2[index]=summary(model)$r.squared
df$nbtokens[index]=nb_tokens
}
}
}
}
return(R2_results=as_tibble(df))
}R2_comp=R2_by_parameters(DATA, seq(8,18), AG_g, CORPUS, "understands")
R2_prod=R2_by_parameters(DATA, seq(8,30), AG_g, CORPUS, "produces")Saving results
#write.table(R2_comp, paste(res, "/R2_eng_all_corpus_comprehension_BraginskyCDIData.csv", sep=""), na = "NA", append = FALSE, col.names = TRUE, sep="\t", row.names = FALSE)
#write.table(R2_prod, paste(res, "/R2_eng_all_corpus_production_BraginskyCDIData.csv", sep=""), na = "NA", append = FALSE, col.names = TRUE, sep="\t", row.names = FALSE)Visualising results
ggplot(R2_comp%>%filter(au%in% c("gold","tp/relativeforward/syllable",
"tp/relativebackward/syllable","ag/syllable")), aes(as.factor(age), R2, colour=au, shape=corpus))+
geom_point(size=4, alpha=0.9) +
facet_grid(. ~ corpus)+
theme_bw(base_size=22) 
ggplot(R2_prod%>%filter(au%in% c("gold","tp/relativeforward/syllable", "tp/relativebackward/syllable", "ag/syllable"))
, aes(as.factor(age), R2, colour=au, shape=corpus))+
geom_point(size=4, alpha=0.9) +
facet_grid( ~ corpus)+
theme_bw(base_size=22) 
LM
Let’s check results from IS paper : corpus==brent, measure==understands, age==13
AGE=13
MEASURE="understands"
CORPUS="brent"
sub<-DATA%>%filter(age==AGE, measure==MEASURE, corpus==CORPUS)
sub$au=factor(sub$au)
sub$au=relevel(sub$au,"gold")
m0 <- lm(prop ~ log(count+1)+ log(count+1):au, sub)
summary(m0)##
## Call:
## lm(formula = prop ~ log(count + 1) + log(count + 1):au, data = sub)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.37385 -0.16064 -0.05532 0.12582 0.76014
##
## Coefficients:
## Estimate Std. Error
## (Intercept) 2.152e-01 5.035e-03
## log(count + 1) 1.943e-02 2.533e-03
## log(count + 1):auag/phoneme -6.145e-05 3.346e-03
## log(count + 1):auag/syllable 6.909e-03 3.719e-03
## log(count + 1):audibs/phoneme 2.191e-03 4.362e-03
## log(count + 1):audibs/syllable -2.449e-03 3.549e-03
## log(count + 1):aupuddle/phoneme -5.878e-04 3.495e-03
## log(count + 1):aupuddle/syllable 1.841e-03 3.510e-03
## log(count + 1):autp/absolutebackward/phoneme -4.012e-05 4.375e-03
## log(count + 1):autp/absolutebackward/syllable 4.545e-03 3.620e-03
## log(count + 1):autp/absoluteforward/phoneme 4.215e-03 4.163e-03
## log(count + 1):autp/absoluteforward/syllable 3.017e-03 3.450e-03
## log(count + 1):autp/relativebackward/phoneme 2.847e-03 3.932e-03
## log(count + 1):autp/relativebackward/syllable 1.105e-02 4.150e-03
## log(count + 1):autp/relativeforward/phoneme 1.922e-03 3.813e-03
## log(count + 1):autp/relativeforward/syllable 6.490e-03 3.700e-03
## t value Pr(>|t|)
## (Intercept) 42.742 < 2e-16 ***
## log(count + 1) 7.672 2.03e-14 ***
## log(count + 1):auag/phoneme -0.018 0.98535
## log(count + 1):auag/syllable 1.858 0.06324 .
## log(count + 1):audibs/phoneme 0.502 0.61548
## log(count + 1):audibs/syllable -0.690 0.49028
## log(count + 1):aupuddle/phoneme -0.168 0.86644
## log(count + 1):aupuddle/syllable 0.524 0.60000
## log(count + 1):autp/absolutebackward/phoneme -0.009 0.99268
## log(count + 1):autp/absolutebackward/syllable 1.256 0.20930
## log(count + 1):autp/absoluteforward/phoneme 1.012 0.31144
## log(count + 1):autp/absoluteforward/syllable 0.874 0.38189
## log(count + 1):autp/relativebackward/phoneme 0.724 0.46903
## log(count + 1):autp/relativebackward/syllable 2.662 0.00779 **
## log(count + 1):autp/relativeforward/phoneme 0.504 0.61415
## log(count + 1):autp/relativeforward/syllable 1.754 0.07950 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.2107 on 4919 degrees of freedom
## Multiple R-squared: 0.06188, Adjusted R-squared: 0.05902
## F-statistic: 21.63 on 15 and 4919 DF, p-value: < 2.2e-16plot_model(m0, type="std", sort.est = TRUE, show.values = TRUE, value.offset = .3, axis.lim = c(-0.5, 0.5))
Let’s for each algorithm compare it predictive power to the gold
Only TPs RF with syllable
pair=c("tp/relativeforward/syllable", "gold", "ag/syllable")
m1=lm(prop~log(count+1) + log(count+1):au ,data=sub,subset=c(au %in% pair))
summary(m1)##
## Call:
## lm(formula = prop ~ log(count + 1) + log(count + 1):au, data = sub,
## subset = c(au %in% pair))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.41816 -0.13789 -0.03344 0.11441 0.62039
##
## Coefficients:
## Estimate Std. Error t value
## (Intercept) 0.057267 0.016459 3.479
## log(count + 1) 0.049477 0.003816 12.965
## log(count + 1):auag/syllable 0.012499 0.003504 3.567
## log(count + 1):autp/relativeforward/syllable 0.012285 0.003490 3.520
## Pr(>|t|)
## (Intercept) 0.000525 ***
## log(count + 1) < 2e-16 ***
## log(count + 1):auag/syllable 0.000378 ***
## log(count + 1):autp/relativeforward/syllable 0.000451 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.196 on 983 degrees of freedom
## Multiple R-squared: 0.1891, Adjusted R-squared: 0.1866
## F-statistic: 76.42 on 3 and 983 DF, p-value: < 2.2e-16plot_model(m1, type="std", sort.est = TRUE, show.values = TRUE, value.offset = .3, axis.lim=c(0,0.5))
Look at the evoluation of age for all algorithms : only TP RF syllable has 1. an effect > 0.1, that is 2. significant
sub<-DATA%>%filter(measure==MEASURE, corpus==CORPUS)
sub$au=factor(sub$au)
sub$au=relevel(sub$au,"gold")
m2 <- lm(prop ~ log(count+1)*age+ log(count+1):au:age, sub)
summary(m2)##
## Call:
## lm(formula = prop ~ log(count + 1) * age + log(count + 1):au:age,
## data = sub)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.56112 -0.12793 -0.02549 0.10337 0.91611
##
## Coefficients:
## Estimate Std. Error
## (Intercept) -3.905e-01 5.841e-03
## log(count + 1) -2.228e-03 1.430e-03
## age 4.859e-02 4.368e-04
## log(count + 1):age 1.284e-03 1.163e-04
## log(count + 1):age:auag/phoneme 2.415e-06 6.910e-05
## log(count + 1):age:auag/syllable 4.042e-04 7.681e-05
## log(count + 1):age:audibs/phoneme 1.054e-04 9.010e-05
## log(count + 1):age:audibs/syllable -1.349e-04 7.330e-05
## log(count + 1):age:aupuddle/phoneme -3.483e-05 7.219e-05
## log(count + 1):age:aupuddle/syllable 9.986e-05 7.250e-05
## log(count + 1):age:autp/absolutebackward/phoneme -5.929e-05 9.036e-05
## log(count + 1):age:autp/absolutebackward/syllable 2.659e-04 7.477e-05
## log(count + 1):age:autp/absoluteforward/phoneme 2.132e-04 8.599e-05
## log(count + 1):age:autp/absoluteforward/syllable 1.974e-04 7.125e-05
## log(count + 1):age:autp/relativebackward/phoneme 1.616e-04 8.121e-05
## log(count + 1):age:autp/relativebackward/syllable 6.021e-04 8.572e-05
## log(count + 1):age:autp/relativeforward/phoneme 1.087e-04 7.875e-05
## log(count + 1):age:autp/relativeforward/syllable 4.075e-04 7.643e-05
## t value Pr(>|t|)
## (Intercept) -66.853 < 2e-16 ***
## log(count + 1) -1.558 0.119323
## age 111.236 < 2e-16 ***
## log(count + 1):age 11.038 < 2e-16 ***
## log(count + 1):age:auag/phoneme 0.035 0.972116
## log(count + 1):age:auag/syllable 5.262 1.43e-07 ***
## log(count + 1):age:audibs/phoneme 1.170 0.241975
## log(count + 1):age:audibs/syllable -1.841 0.065667 .
## log(count + 1):age:aupuddle/phoneme -0.482 0.629472
## log(count + 1):age:aupuddle/syllable 1.377 0.168407
## log(count + 1):age:autp/absolutebackward/phoneme -0.656 0.511731
## log(count + 1):age:autp/absolutebackward/syllable 3.556 0.000377 ***
## log(count + 1):age:autp/absoluteforward/phoneme 2.479 0.013171 *
## log(count + 1):age:autp/absoluteforward/syllable 2.770 0.005604 **
## log(count + 1):age:autp/relativebackward/phoneme 1.990 0.046620 *
## log(count + 1):age:autp/relativebackward/syllable 7.024 2.18e-12 ***
## log(count + 1):age:autp/relativeforward/phoneme 1.380 0.167635
## log(count + 1):age:autp/relativeforward/syllable 5.332 9.76e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.1931 on 54267 degrees of freedom
## Multiple R-squared: 0.4469, Adjusted R-squared: 0.4467
## F-statistic: 2579 on 17 and 54267 DF, p-value: < 2.2e-16plot_model(m2, type="std", sort.est = TRUE, show.values = TRUE, axis.lim=as.vector(c(0,0.5)))
#plotting interaction between age and algorithm-unit pair
#plot_model(m2, type = "int", terms = c("age", "au")) # not working !!!Restriction to TP-RF syllable and gold
included=c("tp/relativeforward/syllable", "gold", "ag/syllable")
sub<-DATA%>%filter(measure==MEASURE, corpus==CORPUS, au %in% included)
sub$au=factor(sub$au)
sub$au=relevel(sub$au,"gold")
m2_tp <- lm(prop ~ age+ log(count+1):age+ log(count+1):au:age, sub)
#m2_tp <- lm(prop ~ age + log():age+ log(freq_smoothed):au:age, sub)
summary(m2_tp)##
## Call:
## lm(formula = prop ~ age + log(count + 1):age + log(count + 1):au:age,
## data = sub)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.58230 -0.11345 -0.01771 0.09954 0.83078
##
## Coefficients:
## Estimate Std. Error
## (Intercept) -3.978e-01 7.425e-03
## age 3.965e-02 6.407e-04
## age:log(count + 1) 2.924e-03 8.023e-05
## age:log(count + 1):auag/syllable 7.394e-04 7.366e-05
## age:log(count + 1):autp/relativeforward/syllable 7.550e-04 7.337e-05
## t value Pr(>|t|)
## (Intercept) -53.58 <2e-16 ***
## age 61.88 <2e-16 ***
## age:log(count + 1) 36.44 <2e-16 ***
## age:log(count + 1):auag/syllable 10.04 <2e-16 ***
## age:log(count + 1):autp/relativeforward/syllable 10.29 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.1829 on 10852 degrees of freedom
## Multiple R-squared: 0.5043, Adjusted R-squared: 0.5041
## F-statistic: 2760 on 4 and 10852 DF, p-value: < 2.2e-16plot_model(m2_tp, type="std", sort.est = TRUE, show.values = TRUE, axis.lim=as.vector(c(0,1)))
Weird fact : the p-value with count is clearly not the same than with freq_smoothed!
m=lm(prop~ log_freq*as.factor(age)*au, sub)
plot_model(m, type="pred", terms=c("log_freq", "au", "age"))
Let’s for each algorithm compare it predictive power to the gold with age evolution
for (a in AU)
{
included=c(a, "gold")
#fit2=lm(prop~ age+ log(count+1):age + log(count+1):au:age ,data=sub,subset=c(au %in% included))
#print(summary(fit2))
}Now corpus=“brent”, measure=“produces”, algos=‘TP-RF-syll’
MEASURE="produces"
CORPUS="brent"
included=c("tp/relativeforward/syllable", "gold")
sub<-DATA%>%filter(measure==MEASURE, corpus==CORPUS, au %in% included)
sub$au=factor(sub$au)
sub$au=relevel(sub$au,"gold")
m_b_prod <- lm(prop ~ age + log(count+1):age+ log(count+1):au:age, sub)
summary(m_b_prod)##
## Call:
## lm(formula = prop ~ age + log(count + 1):age + log(count + 1):au:age,
## data = sub)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.49142 -0.10785 0.00057 0.10606 0.68676
##
## Coefficients:
## Estimate Std. Error
## (Intercept) -4.837e-01 3.797e-03
## age 3.707e-02 2.481e-04
## age:log(count + 1) 1.237e-03 3.747e-05
## age:log(count + 1):autp/relativeforward/syllable 3.805e-04 2.875e-05
## t value Pr(>|t|)
## (Intercept) -127.41 <2e-16 ***
## age 149.41 <2e-16 ***
## age:log(count + 1) 33.01 <2e-16 ***
## age:log(count + 1):autp/relativeforward/syllable 13.23 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.1538 on 15082 degrees of freedom
## Multiple R-squared: 0.7791, Adjusted R-squared: 0.779
## F-statistic: 1.773e+04 on 3 and 15082 DF, p-value: < 2.2e-16plot_model(m_b_prod, type="std", sort.est = TRUE, show.values = TRUE, value.offset = .3)
Now corpus=“providence”, measure=“understands”, algos=‘TP-RF-syll’
MEASURE="understands"
CORPUS="providence"
included=c("tp/relativeforward/syllable", "gold")
sub<-DATA%>%filter(measure==MEASURE, corpus==CORPUS, au %in% included)
sub$au=factor(sub$au)
sub$au=relevel(sub$au,"gold")
m_b_prod <- lm(prop ~ age + log(count+1):age+ log(count+1):au:age, sub)
summary(m_b_prod)##
## Call:
## lm(formula = prop ~ age + log(count + 1):age + log(count + 1):au:age,
## data = sub)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.51882 -0.12891 -0.02384 0.10682 0.86587
##
## Coefficients:
## Estimate Std. Error
## (Intercept) -4.025e-01 9.642e-03
## age 4.797e-02 8.500e-04
## age:log(count + 1) 1.342e-03 1.162e-04
## age:log(count + 1):autp/relativeforward/syllable 5.348e-04 9.435e-05
## t value Pr(>|t|)
## (Intercept) -41.746 < 2e-16 ***
## age 56.433 < 2e-16 ***
## age:log(count + 1) 11.549 < 2e-16 ***
## age:log(count + 1):autp/relativeforward/syllable 5.668 1.5e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.1945 on 7278 degrees of freedom
## Multiple R-squared: 0.4401, Adjusted R-squared: 0.4399
## F-statistic: 1907 on 3 and 7278 DF, p-value: < 2.2e-16plot_model(m_b_prod, type="std", sort.est = TRUE, show.values = TRUE, value.offset = .3)
Now corpus=“providence”, measure=“produces”, algos=‘TP-RF-syll’
MEASURE="produces"
CORPUS="providence"
included=c("tp/relativeforward/syllable", "gold")
sub<-DATA%>%filter(measure==MEASURE, corpus==CORPUS, au %in% included)
sub$au=factor(sub$au)
sub$au=relevel(sub$au,"gold")
m_b_prod <- lm(prop ~ age + log(count+1):age+ log(count+1):au:age, sub)
summary(m_b_prod)##
## Call:
## lm(formula = prop ~ age + log(count + 1):age + log(count + 1):au:age,
## data = sub)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.53169 -0.11140 0.00001 0.10605 0.68784
##
## Coefficients:
## Estimate Std. Error
## (Intercept) -4.843e-01 3.877e-03
## age 3.912e-02 2.557e-04
## age:log(count + 1) 9.080e-04 4.354e-05
## age:log(count + 1):autp/relativeforward/syllable 4.151e-04 3.526e-05
## t value Pr(>|t|)
## (Intercept) -124.94 <2e-16 ***
## age 152.98 <2e-16 ***
## age:log(count + 1) 20.85 <2e-16 ***
## age:log(count + 1):autp/relativeforward/syllable 11.77 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.1576 on 15174 degrees of freedom
## Multiple R-squared: 0.7689, Adjusted R-squared: 0.7688
## F-statistic: 1.683e+04 on 3 and 15174 DF, p-value: < 2.2e-16plot_model(m_b_prod, type="std", sort.est = TRUE, show.values = TRUE, value.offset = .3)
Now corpus=“buckeye”, measure=“understands”, algos=‘TP-RF-syll’
MEASURE="understands"
CORPUS="buckeye"
included=c("tp/relativeforward/syllable", "gold")
sub<-DATA%>%filter(measure==MEASURE, corpus==CORPUS, au %in% included)
sub$au=factor(sub$au)
sub$au=relevel(sub$au,"gold")
m_b_prod <- lm(prop ~ age + log(count+1):age+ log(count+1):au:age, sub)
summary(m_b_prod)##
## Call:
## lm(formula = prop ~ age + log(count + 1):age + log(count + 1):au:age,
## data = sub)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.52574 -0.12719 -0.02325 0.10246 0.88614
##
## Coefficients:
## Estimate Std. Error
## (Intercept) -0.3976710 0.0106862
## age 0.0559301 0.0008372
## age:log(count + 1) -0.0010435 0.0001012
## age:log(count + 1):autp/relativeforward/syllable -0.0004653 0.0001312
## t value Pr(>|t|)
## (Intercept) -37.213 < 2e-16 ***
## age 66.803 < 2e-16 ***
## age:log(count + 1) -10.307 < 2e-16 ***
## age:log(count + 1):autp/relativeforward/syllable -3.545 0.000396 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.1925 on 5804 degrees of freedom
## Multiple R-squared: 0.4385, Adjusted R-squared: 0.4382
## F-statistic: 1511 on 3 and 5804 DF, p-value: < 2.2e-16plot_model(m_b_prod, type="std", sort.est = TRUE, show.values = TRUE, value.offset = .3)
Now corpus=“buckeye”, measure=“produces”, algos=‘TP-RF-syll’
MEASURE="produces"
CORPUS="buckeye"
included=c("tp/relativeforward/syllable", "gold")
sub<-DATA%>%filter(measure==MEASURE, corpus==CORPUS, au %in% included)
sub$au=factor(sub$au)
sub$au=relevel(sub$au,"gold")
m_b_prod <- lm(prop ~ age + log(count+1):age+ log(count+1):au:age, sub)
summary(m_b_prod)##
## Call:
## lm(formula = prop ~ age + log(count + 1):age + log(count + 1):au:age,
## data = sub)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.51414 -0.11461 -0.00039 0.10707 0.71553
##
## Coefficients:
## Estimate Std. Error
## (Intercept) -4.859e-01 4.357e-03
## age 4.458e-02 2.365e-04
## age:log(count + 1) -6.385e-04 3.857e-05
## age:log(count + 1):autp/relativeforward/syllable -2.264e-04 4.998e-05
## t value Pr(>|t|)
## (Intercept) -111.51 < 2e-16 ***
## age 188.48 < 2e-16 ***
## age:log(count + 1) -16.55 < 2e-16 ***
## age:log(count + 1):autp/relativeforward/syllable -4.53 5.94e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.1581 on 12092 degrees of freedom
## Multiple R-squared: 0.7649, Adjusted R-squared: 0.7648
## F-statistic: 1.311e+04 on 3 and 12092 DF, p-value: < 2.2e-16plot_model(m_b_prod, type="std", sort.est = TRUE, show.values = TRUE, value.offset = .3)
Linear mixed effect model
One big model : corpus and measure included and interacting with algorithm-unit and log frequency
Let’s fix age
AGE=13
included=c("gold", "tp/relativeforward/syllable")
x<-DATA%>%filter(age==AGE, au %in% included)
x$au=as.factor(x$au)
x$corpus=as.factor(x$corpus)
x$measure=as.factor(x$measure)
x$au=relevel(x$au,"gold")
x$corpus=relevel(x$corpus,"buckeye")
x$measure=relevel(x$measure, "understands")
m3 <- lmer(prop ~ log(freq_smoothed)+ log(freq_smoothed):au:corpus:measure +(1 + log(freq_smoothed)|uni_lemma), x)
#m3 <- lmer(prop ~ log(count+1)+ log(count+1):au:corpus:measure +(1 + log(count+1)|uni_lemma), x)
summary(m3)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## prop ~ log(freq_smoothed) + log(freq_smoothed):au:corpus:measure +
## (1 + log(freq_smoothed) | uni_lemma)
## Data: x
##
## REML criterion at convergence: -5441.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.0434 -0.7246 -0.0130 0.6959 3.3538
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## uni_lemma (Intercept) 1.987e-02 0.14095
## log(freq_smoothed) 1.254e-06 0.00112 1.00
## Residual 9.841e-03 0.09920
## Number of obs: 3696, groups: uni_lemma, 336
##
## Fixed effects:
## Estimate
## (Intercept) 1.813e-01
## log(freq_smoothed) 1.766e-02
## log(freq_smoothed):augold:corpusbuckeye:measureunderstands -2.829e-02
## log(freq_smoothed):autp/relativeforward/syllable:corpusbuckeye:measureunderstands -2.875e-02
## log(freq_smoothed):augold:corpusbrent:measureunderstands -2.899e-02
## log(freq_smoothed):autp/relativeforward/syllable:corpusbrent:measureunderstands -2.936e-02
## log(freq_smoothed):augold:corpusprovidence:measureunderstands -2.968e-02
## log(freq_smoothed):autp/relativeforward/syllable:corpusprovidence:measureunderstands -3.017e-02
## log(freq_smoothed):augold:corpusbuckeye:measureproduces -2.974e-03
## log(freq_smoothed):autp/relativeforward/syllable:corpusbuckeye:measureproduces -2.301e-03
## log(freq_smoothed):augold:corpusbrent:measureproduces -1.201e-03
## log(freq_smoothed):autp/relativeforward/syllable:corpusbrent:measureproduces -6.303e-04
## log(freq_smoothed):augold:corpusprovidence:measureproduces -7.107e-04
## Std. Error
## (Intercept) 1.733e-02
## log(freq_smoothed) 2.027e-03
## log(freq_smoothed):augold:corpusbuckeye:measureunderstands 9.888e-04
## log(freq_smoothed):autp/relativeforward/syllable:corpusbuckeye:measureunderstands 9.826e-04
## log(freq_smoothed):augold:corpusbrent:measureunderstands 9.352e-04
## log(freq_smoothed):autp/relativeforward/syllable:corpusbrent:measureunderstands 9.484e-04
## log(freq_smoothed):augold:corpusprovidence:measureunderstands 9.308e-04
## log(freq_smoothed):autp/relativeforward/syllable:corpusprovidence:measureunderstands 9.463e-04
## log(freq_smoothed):augold:corpusbuckeye:measureproduces 9.888e-04
## log(freq_smoothed):autp/relativeforward/syllable:corpusbuckeye:measureproduces 9.826e-04
## log(freq_smoothed):augold:corpusbrent:measureproduces 9.352e-04
## log(freq_smoothed):autp/relativeforward/syllable:corpusbrent:measureproduces 9.484e-04
## log(freq_smoothed):augold:corpusprovidence:measureproduces 9.308e-04
## df
## (Intercept) 1.138e+03
## log(freq_smoothed) 3.536e+03
## log(freq_smoothed):augold:corpusbuckeye:measureunderstands 3.441e+03
## log(freq_smoothed):autp/relativeforward/syllable:corpusbuckeye:measureunderstands 3.420e+03
## log(freq_smoothed):augold:corpusbrent:measureunderstands 3.349e+03
## log(freq_smoothed):autp/relativeforward/syllable:corpusbrent:measureunderstands 3.346e+03
## log(freq_smoothed):augold:corpusprovidence:measureunderstands 3.346e+03
## log(freq_smoothed):autp/relativeforward/syllable:corpusprovidence:measureunderstands 3.342e+03
## log(freq_smoothed):augold:corpusbuckeye:measureproduces 3.441e+03
## log(freq_smoothed):autp/relativeforward/syllable:corpusbuckeye:measureproduces 3.420e+03
## log(freq_smoothed):augold:corpusbrent:measureproduces 3.349e+03
## log(freq_smoothed):autp/relativeforward/syllable:corpusbrent:measureproduces 3.346e+03
## log(freq_smoothed):augold:corpusprovidence:measureproduces 3.346e+03
## t value
## (Intercept) 10.459
## log(freq_smoothed) 8.711
## log(freq_smoothed):augold:corpusbuckeye:measureunderstands -28.616
## log(freq_smoothed):autp/relativeforward/syllable:corpusbuckeye:measureunderstands -29.257
## log(freq_smoothed):augold:corpusbrent:measureunderstands -31.000
## log(freq_smoothed):autp/relativeforward/syllable:corpusbrent:measureunderstands -30.958
## log(freq_smoothed):augold:corpusprovidence:measureunderstands -31.885
## log(freq_smoothed):autp/relativeforward/syllable:corpusprovidence:measureunderstands -31.885
## log(freq_smoothed):augold:corpusbuckeye:measureproduces -3.008
## log(freq_smoothed):autp/relativeforward/syllable:corpusbuckeye:measureproduces -2.342
## log(freq_smoothed):augold:corpusbrent:measureproduces -1.285
## log(freq_smoothed):autp/relativeforward/syllable:corpusbrent:measureproduces -0.665
## log(freq_smoothed):augold:corpusprovidence:measureproduces -0.763
## Pr(>|t|)
## (Intercept) < 2e-16
## log(freq_smoothed) < 2e-16
## log(freq_smoothed):augold:corpusbuckeye:measureunderstands < 2e-16
## log(freq_smoothed):autp/relativeforward/syllable:corpusbuckeye:measureunderstands < 2e-16
## log(freq_smoothed):augold:corpusbrent:measureunderstands < 2e-16
## log(freq_smoothed):autp/relativeforward/syllable:corpusbrent:measureunderstands < 2e-16
## log(freq_smoothed):augold:corpusprovidence:measureunderstands < 2e-16
## log(freq_smoothed):autp/relativeforward/syllable:corpusprovidence:measureunderstands < 2e-16
## log(freq_smoothed):augold:corpusbuckeye:measureproduces 0.00265
## log(freq_smoothed):autp/relativeforward/syllable:corpusbuckeye:measureproduces 0.01923
## log(freq_smoothed):augold:corpusbrent:measureproduces 0.19904
## log(freq_smoothed):autp/relativeforward/syllable:corpusbrent:measureproduces 0.50633
## log(freq_smoothed):augold:corpusprovidence:measureproduces 0.44524
##
## (Intercept) ***
## log(freq_smoothed) ***
## log(freq_smoothed):augold:corpusbuckeye:measureunderstands ***
## log(freq_smoothed):autp/relativeforward/syllable:corpusbuckeye:measureunderstands ***
## log(freq_smoothed):augold:corpusbrent:measureunderstands ***
## log(freq_smoothed):autp/relativeforward/syllable:corpusbrent:measureunderstands ***
## log(freq_smoothed):augold:corpusprovidence:measureunderstands ***
## log(freq_smoothed):autp/relativeforward/syllable:corpusprovidence:measureunderstands ***
## log(freq_smoothed):augold:corpusbuckeye:measureproduces **
## log(freq_smoothed):autp/relativeforward/syllable:corpusbuckeye:measureproduces *
## log(freq_smoothed):augold:corpusbrent:measureproduces
## log(freq_smoothed):autp/relativeforward/syllable:corpusbrent:measureproduces
## log(freq_smoothed):augold:corpusprovidence:measureproduces
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## fit warnings:
## fixed-effect model matrix is rank deficient so dropping 1 column / coefficient#plot_model(m3, type = "std", sort.est = TRUE, show.values = TRUE) # not working !!!!Interaction between corpus and measure and algorithm by 18 month
MEASURE="produces"
AGE=25
if(MEASURE=="understands"){task="comprehension"}else{task="production"}
included=c("gold", "tp/relativeforward/syllable", "ag/syllable")
x<-DATA%>%filter(age==AGE, au %in% included, measure==MEASURE)
x$au=as.factor(x$au)
x$corpus=as.factor(x$corpus)
x$au=relevel(x$au,"gold")
x$corpus=relevel(x$corpus,"buckeye")
#m <- lmer(prop ~ log_freq*as.factor(au)*as.factor(corpus) + (log(freq_smoothed)|uni_lemma) , x)
m <- lm(prop ~ log_count*au*corpus, x)
plot_model(m, type="pred", terms=c("log_count", "au", "corpus"), title=paste("Predicted values for proportion of", AGE, "mo infants", task, sep=" "))
MEASURE="understands"
AGE=16
if(MEASURE=="understands"){task="comprehension"}else{task="production"}
included=c("gold", "tp/relativeforward/syllable", "ag/syllable")
x<-DATA%>%filter(age==AGE, au %in% included, measure==MEASURE)
x$au=as.factor(x$au)
x$corpus=as.factor(x$corpus)
x$au=relevel(x$au,"gold")
x$corpus=relevel(x$corpus,"buckeye")
#m <- lmer(prop ~ log_freq*as.factor(au)*as.factor(corpus) + (log(freq_smoothed)|uni_lemma) , x)
m <- lm(prop ~ log_count*au*corpus, x)
plot_model(m, type="pred", terms=c("log_count", "au", "corpus"), title=paste("Predicted values for proportion of", AGE, "mo infants", task, sep=" "))
Interesting right ? It seems (though I would love to properly test statistically and have the statistical power to do so) that TP-RF syllable prediction are clearly separated for comprehension for the Brent and Providnece corpus but not at all for the buckeye corpus. For production at 18 mo prediction differences are not so clear : so let’s look at the evolution of these predictions across ages.
With age as a main effect, which slopes depends The thing is : the comprehension measure is only available from 8 to 18 mo, while the production one is available from 8 to 30 mo.
included=c("gold", "tp/relativeforward/syllable")
x<-DATA%>%filter(au %in% included)
x$au=as.factor(x$au)
x$corpus=as.factor(x$corpus)
x$measure=as.factor(x$measure)
x$au=relevel(x$au,"gold")
x$corpus=relevel(x$corpus,"buckeye")
x$measure=relevel(x$measure, "understands")
m4 <- lmer(prop ~ age*log(freq_smoothed)+ log(freq_smoothed):au:corpus:measure +(1+age+ log(freq_smoothed)|uni_lemma), x)
summary(m4)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## prop ~ age * log(freq_smoothed) + log(freq_smoothed):au:corpus:measure +
## (1 + age + log(freq_smoothed) | uni_lemma)
## Data: x
##
## REML criterion at convergence: -103058.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.9019 -0.7063 -0.0639 0.5986 4.1477
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## uni_lemma (Intercept) 1.749e-02 0.1322546
## age 7.102e-05 0.0084271 -0.54
## log(freq_smoothed) 1.129e-08 0.0001062 0.62 0.32
## Residual 1.073e-02 0.1035820
## Number of obs: 62688, groups: uni_lemma, 336
##
## Fixed effects:
## Estimate
## (Intercept) -3.275e-01
## age 3.809e-02
## log(freq_smoothed) 1.995e-02
## age:log(freq_smoothed) -6.462e-04
## log(freq_smoothed):augold:corpusbuckeye:measureunderstands -2.440e-02
## log(freq_smoothed):autp/relativeforward/syllable:corpusbuckeye:measureunderstands -2.500e-02
## log(freq_smoothed):augold:corpusbrent:measureunderstands -2.573e-02
## log(freq_smoothed):autp/relativeforward/syllable:corpusbrent:measureunderstands -2.626e-02
## log(freq_smoothed):augold:corpusprovidence:measureunderstands -2.628e-02
## log(freq_smoothed):autp/relativeforward/syllable:corpusprovidence:measureunderstands -2.693e-02
## log(freq_smoothed):augold:corpusbuckeye:measureproduces -1.284e-03
## log(freq_smoothed):autp/relativeforward/syllable:corpusbuckeye:measureproduces -9.831e-04
## log(freq_smoothed):augold:corpusbrent:measureproduces -5.804e-04
## log(freq_smoothed):autp/relativeforward/syllable:corpusbrent:measureproduces -3.103e-04
## log(freq_smoothed):augold:corpusprovidence:measureproduces -3.289e-04
## Std. Error
## (Intercept) 1.214e-02
## age 6.952e-04
## log(freq_smoothed) 1.146e-03
## age:log(freq_smoothed) 6.029e-05
## log(freq_smoothed):augold:corpusbuckeye:measureunderstands 2.696e-04
## log(freq_smoothed):autp/relativeforward/syllable:corpusbuckeye:measureunderstands 2.692e-04
## log(freq_smoothed):augold:corpusbrent:measureunderstands 2.572e-04
## log(freq_smoothed):autp/relativeforward/syllable:corpusbrent:measureunderstands 2.630e-04
## log(freq_smoothed):augold:corpusprovidence:measureunderstands 2.559e-04
## log(freq_smoothed):autp/relativeforward/syllable:corpusprovidence:measureunderstands 2.626e-04
## log(freq_smoothed):augold:corpusbuckeye:measureproduces 2.249e-04
## log(freq_smoothed):autp/relativeforward/syllable:corpusbuckeye:measureproduces 2.209e-04
## log(freq_smoothed):augold:corpusbrent:measureproduces 2.045e-04
## log(freq_smoothed):autp/relativeforward/syllable:corpusbrent:measureproduces 2.070e-04
## log(freq_smoothed):augold:corpusprovidence:measureproduces 2.033e-04
## df
## (Intercept) 1.808e+03
## age 1.545e+03
## log(freq_smoothed) 4.813e+04
## age:log(freq_smoothed) 4.964e+04
## log(freq_smoothed):augold:corpusbuckeye:measureunderstands 6.211e+04
## log(freq_smoothed):autp/relativeforward/syllable:corpusbuckeye:measureunderstands 6.217e+04
## log(freq_smoothed):augold:corpusbrent:measureunderstands 6.199e+04
## log(freq_smoothed):autp/relativeforward/syllable:corpusbrent:measureunderstands 6.204e+04
## log(freq_smoothed):augold:corpusprovidence:measureunderstands 6.201e+04
## log(freq_smoothed):autp/relativeforward/syllable:corpusprovidence:measureunderstands 6.207e+04
## log(freq_smoothed):augold:corpusbuckeye:measureproduces 6.197e+04
## log(freq_smoothed):autp/relativeforward/syllable:corpusbuckeye:measureproduces 6.212e+04
## log(freq_smoothed):augold:corpusbrent:measureproduces 6.201e+04
## log(freq_smoothed):autp/relativeforward/syllable:corpusbrent:measureproduces 6.201e+04
## log(freq_smoothed):augold:corpusprovidence:measureproduces 6.201e+04
## t value
## (Intercept) -26.985
## age 54.783
## log(freq_smoothed) 17.413
## age:log(freq_smoothed) -10.719
## log(freq_smoothed):augold:corpusbuckeye:measureunderstands -90.496
## log(freq_smoothed):autp/relativeforward/syllable:corpusbuckeye:measureunderstands -92.844
## log(freq_smoothed):augold:corpusbrent:measureunderstands -100.029
## log(freq_smoothed):autp/relativeforward/syllable:corpusbrent:measureunderstands -99.871
## log(freq_smoothed):augold:corpusprovidence:measureunderstands -102.682
## log(freq_smoothed):autp/relativeforward/syllable:corpusprovidence:measureunderstands -102.559
## log(freq_smoothed):augold:corpusbuckeye:measureproduces -5.710
## log(freq_smoothed):autp/relativeforward/syllable:corpusbuckeye:measureproduces -4.451
## log(freq_smoothed):augold:corpusbrent:measureproduces -2.838
## log(freq_smoothed):autp/relativeforward/syllable:corpusbrent:measureproduces -1.499
## log(freq_smoothed):augold:corpusprovidence:measureproduces -1.618
## Pr(>|t|)
## (Intercept) < 2e-16
## age < 2e-16
## log(freq_smoothed) < 2e-16
## age:log(freq_smoothed) < 2e-16
## log(freq_smoothed):augold:corpusbuckeye:measureunderstands < 2e-16
## log(freq_smoothed):autp/relativeforward/syllable:corpusbuckeye:measureunderstands < 2e-16
## log(freq_smoothed):augold:corpusbrent:measureunderstands < 2e-16
## log(freq_smoothed):autp/relativeforward/syllable:corpusbrent:measureunderstands < 2e-16
## log(freq_smoothed):augold:corpusprovidence:measureunderstands < 2e-16
## log(freq_smoothed):autp/relativeforward/syllable:corpusprovidence:measureunderstands < 2e-16
## log(freq_smoothed):augold:corpusbuckeye:measureproduces 1.13e-08
## log(freq_smoothed):autp/relativeforward/syllable:corpusbuckeye:measureproduces 8.57e-06
## log(freq_smoothed):augold:corpusbrent:measureproduces 0.00454
## log(freq_smoothed):autp/relativeforward/syllable:corpusbrent:measureproduces 0.13394
## log(freq_smoothed):augold:corpusprovidence:measureproduces 0.10562
##
## (Intercept) ***
## age ***
## log(freq_smoothed) ***
## age:log(freq_smoothed) ***
## log(freq_smoothed):augold:corpusbuckeye:measureunderstands ***
## log(freq_smoothed):autp/relativeforward/syllable:corpusbuckeye:measureunderstands ***
## log(freq_smoothed):augold:corpusbrent:measureunderstands ***
## log(freq_smoothed):autp/relativeforward/syllable:corpusbrent:measureunderstands ***
## log(freq_smoothed):augold:corpusprovidence:measureunderstands ***
## log(freq_smoothed):autp/relativeforward/syllable:corpusprovidence:measureunderstands ***
## log(freq_smoothed):augold:corpusbuckeye:measureproduces ***
## log(freq_smoothed):autp/relativeforward/syllable:corpusbuckeye:measureproduces ***
## log(freq_smoothed):augold:corpusbrent:measureproduces **
## log(freq_smoothed):autp/relativeforward/syllable:corpusbrent:measureproduces
## log(freq_smoothed):augold:corpusprovidence:measureproduces
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## fit warnings:
## fixed-effect model matrix is rank deficient so dropping 1 column / coefficientChecking model assumptions : Arghh;…
plot_model(m0, type="diag")## [[1]]
##
## [[2]]
##
## [[3]]
##
## [[4]]
plot_model(m1, type="diag")## [[1]]
##
## [[2]]
##
## [[3]]
##
## [[4]]
plot_model(m2, type="diag")## [[1]]
##
## [[2]]
##
## [[3]]
##
## [[4]]
plot_model(m3, type="diag")## [[1]]
##
## [[2]]
##
## [[3]]
##
## [[4]]